Dynamic control of high-voltage actuator arrays by light-pattern projection on photoconductive switches
Bibliographic record
Abstract
The ability to control high-voltage actuator arrays relies, to date, on expensive microelectronic processes or on individual wiring of each actuator to a single off-chip high-voltage switch. Here we present an alternative approach that uses on-chip photoconductive switches together with a light projection system to individually address high-voltage actuators. Each actuator is connected to one or more switches that are nominally OFF unless turned ON using direct light illumination. We selected hydrogenated amorphous silicon (a-Si:H) as our photoconductive material, and we provide a complete characterization of its light to dark conductance, breakdown field, and spectral response. The resulting switches are very robust, and we provide full details of their fabrication processes. We demonstrate that the switches can be integrated into different architectures to support both AC and DC-driven actuators and provide engineering guidelines for their functional design. To demonstrate the versatility of our approach, we demonstrate the use of the photoconductive switches in two distinctly different applications-control of µm-sized gate electrodes for patterning flow fields in a microfluidic chamber and control of cm-sized electrostatic actuators for creating mechanical deformations for haptic displays.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".